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Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations

Simone Ciccolella, Mauricio Soto Gomez, View ORCID ProfileMurray Patterson, View ORCID ProfileGianluca Della Vedova, Iman Hajirasouliha, Paola Bonizzoni
doi: https://doi.org/10.1101/268243
Simone Ciccolella
1Department of Computer Science, Systems and Communication, Univ. Milano-Bicocca, Milan, Italy
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Mauricio Soto Gomez
1Department of Computer Science, Systems and Communication, Univ. Milano-Bicocca, Milan, Italy
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Murray Patterson
1Department of Computer Science, Systems and Communication, Univ. Milano-Bicocca, Milan, Italy
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Gianluca Della Vedova
1Department of Computer Science, Systems and Communication, Univ. Milano-Bicocca, Milan, Italy
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Iman Hajirasouliha
2Institute for Computational Biomedicine, Weill Cornell Medicine, NY, USA
3Department of Physiology and Biophysics, Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, NY, USA
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Paola Bonizzoni
1Department of Computer Science, Systems and Communication, Univ. Milano-Bicocca, Milan, Italy
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ABSTRACT

Motivation In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogeny trees and inferring cancer progression. However, recent studies leveraging Single Cell Sequencing (SCS) techniques showed evidence of a number of recurrence and mutational loss in several tumor samples, an observation which essentially violates a strict ISA (e.g. [17].)

Results We present the SASC (Simulated Annealing Single Cell inference) tool, a new model and a robust framework based on Simulated Annealing for the inference of cancer progression from the SCS data.

Our main objective is to overcome the limitations of the Infinite Sites Assumption by introducing a version of the Dollo parsimony model which indeed allows the deletion of mutations from the evolutionary history of the tumor. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with other available methods.

Availability The Simulated Annealing Single Cell inference tool (SASC) is open source and available at https://github.com/sciccolella/sasc.

Contact s.ciccolella{at}campus.unimib.it

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 20, 2018.
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Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations
Simone Ciccolella, Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha, Paola Bonizzoni
bioRxiv 268243; doi: https://doi.org/10.1101/268243
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Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations
Simone Ciccolella, Mauricio Soto Gomez, Murray Patterson, Gianluca Della Vedova, Iman Hajirasouliha, Paola Bonizzoni
bioRxiv 268243; doi: https://doi.org/10.1101/268243

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